Advertisement

Identification of Natural Images and Computer Generated Graphics Using Multi-fractal Differences of PRNU

  • Fei PengEmail author
  • Yin Zhu
  • Min Long
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9529)

Abstract

Comparing with computer generated graphics, natural images have higher self-similar and have more delicate and complex texture. Thus, the distribution of multi-fractal dimensions and singular index of natural images general have large variation range. Based on this, multi-fractal spectrum features of photo response non-uniformity noise (PRNU) are used for the identification of natural images and computer generated graphics. 9 dimensions of texture features including the square of the maximum difference in fractal dimension (SMDF), the square of the maximum difference in the singularity indices (SMS) and the variance of fractal dimensions (VF) are extracted from LL, LH, HL sub-bands of PRNU after wavelet decomposition. The identification is accomplished by using LIBSVM classifier. Experimental results and analysis indicate that it can obtain an average identification accuracy of 99.69 %, and it is robust against resizing, JPEG compression, rotation and additive noise.

Keywords

Forensic science Source identification Multi-fractal feature Photo response non-uniformity Natural images Computer generated graphics 

Notes

Acknowledgments

This work was supported in part by project supported by National Natural Science Foundation of China (Grant No. 61572182, 61370225), project supported by Hunan Provincial Natural Science Foundation of China (Grant No. 15JJ2007), supported by the Scientific Research Plan of Hunan Provincial Science and Technology Department of China (2014FJ4161).

References

  1. 1.
    Mandelbrot, B.B.: How long is the coast of Britain. Science 156(3775), 636–638 (1967)CrossRefGoogle Scholar
  2. 2.
    Peli, E.: Contrast in complex images. J. Opt. Soc. Am. A. 7(10), 2032–2040 (1990)CrossRefGoogle Scholar
  3. 3.
    Irfan, M., Stork, D.G.: Multiple visual features for the computer authentication of Jackson Pollock’s drip paintings: beyond box counting and fractals. In: SPIE 7251. Image Processing: Machine Vision Applications II, vol. 7251. SPIE (2009)Google Scholar
  4. 4.
    Chaabouni, A., Boubaker, H., Kherallah, M., Stork, D.G.: Fractal and multi-fractal for arabic offline writer identification. In: IEEE International Conference on Pattern Recognition, pp. 3793–3796. IEEE (2010)Google Scholar
  5. 5.
    Mukundan, R., Hemsley, A.: Tissue image classification using multi-fractal spectra. Int. J. Multimedia Data Eng. Manage. 1(2), 62–75 (2010)CrossRefGoogle Scholar
  6. 6.
    Maeda, J., Novianto, S., Miyashita, A., Saga, S., Suzuki, Y.: Fuzzy region-growing segmentation of natural images using local fractal dimension. In: 14th IEEE International Conference on Pattern Recognition, vol. 2, pp. 991–993. IEEE (1998)Google Scholar
  7. 7.
    Ng, T.T., Chang, S.F., Hsu, J., Xie, L.: Physics-motivated features for distinguishing photographic images and computer generated graphics. In: 13th Annual ACM International Conference on Multimedia, pp. 239–248. ACM (2005)Google Scholar
  8. 8.
    Pan, F., Chen, J.B., Huang, J.W.: Discriminating between photorealistic computer graphics and natural images using fractal geometry. Sci. China Series F: Inf. Sci. 52(2), 329–337 (2009)CrossRefzbMATHGoogle Scholar
  9. 9.
    Lv, Y., Shen, X.J., Wan, G., Chen, H.P.: Blind identification of photorealistic computer generated graphics based on fractal dimensions. In: International Conference on Computer, Communications and Information Technology, Atlantis Press (2014)Google Scholar
  10. 10.
    Peng, F., Liu, J., Long, M.: Identification of natural images and computer generated graphics based on hybrid features. Int. J. Digital Crime and Forensics 4(1), 1–16 (2012)CrossRefGoogle Scholar
  11. 11.
    Li, P., Hu, K.L., Wang, B.H.: Design and application about computing program of material multi-fractal spectrum. J. Nanjing Univ. Aeronaut. Astronaut. 36(1), 77–81 (2004)Google Scholar
  12. 12.
    Pu, X.Q.: Image Recognition Research Based on Multi-Fractal. Northwestern University, Xi’an (2009)Google Scholar
  13. 13.
    Peng, F., Shi, J.L., Long, M.: Identifying photographic images and photorealistic computer generated graphics using multi-fractal spectrum features of PRNU. In: IEEE International Conference on Multimedia and Expo, pp. 1–6. IEEE (2014)Google Scholar
  14. 14.
    Chen, W., Shi, Y., Xuan, G.: Identifying computer generated graphics using HSV color model and statistical moments of characteristic functions. In: IEEE International Conference on Multimedia and Expo, pp. 1123–1126. IEEE (2007)Google Scholar
  15. 15.
    Lyu, S., Farid, H.: How realistic is photorealistic. IEEE Trans. Sig. Proc. 53, 845–850 (2005)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Shi, Y., Chen, W., Xuan, G., Su, W.: Computer graphics identification using genetic algorithm. In: 19th International Conference on Pattern Recognition, pp. 1–4. IEEE (2008)Google Scholar
  17. 17.
    Wang, R.D., Fan, S.J., Zhang, Y.P.: Classifying computer generated graphics and natural imaged based on image contour information. J. Inf. Comput. Sci. 9(10), 2877–2895 (2012)Google Scholar
  18. 18.
    Lukas, J., Fridrich, J., Goljan, M.: Digital camera identification from sensor pattern noise. IEEE Trans. Inf. Forensics Secur. 1(2), 205–214 (2006)CrossRefGoogle Scholar
  19. 19.
    Li, C.T., Li, Y.: Color-decoupled photo response non-uniformity for digital image forensics. IEEE Trans. Circuits Syst. Video Technol. 22(2), 3052–3055 (2010)Google Scholar
  20. 20.
    Dehnie, S., Sencar, T.: Digital image forensics for identifying computer generated and digital camera images. In: IEEE International Conference on Image Processing, pp. 2313–2316. IEEE (2006)Google Scholar
  21. 21.
    Li, C.T.: Source camera identification using enhanced sensor pattern noise. IEEE Trans. Inf. Forensics Secur. 5(2), 280–287 (2010)CrossRefGoogle Scholar
  22. 22.
    Li, H.F.: The Study on Multi-fractal Theory and Application in Image Processing. Northwestern Polytechnical University, Xi’an (2004)Google Scholar
  23. 23.
    Zhou, W.X., Wang, Y.J.: Geometrical characteristics of singularity spectra of multi-fractals: I. Classical Renyi definition. J. East China Univ. Sci. Technol.: Nat. Sci. Ed. 26(4), 385–389 (2000)Google Scholar
  24. 24.
    Columbia dvmm research lab columbia photographic images and photorealistic computer generated graphics dataset [db/dl] (5 February 2005) [12 August 2008]Google Scholar
  25. 25.

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.School of Computer Science and Electronic EngineeringHunan UniversityChangshaChina
  2. 2.College of Computer and Communication EngineeringChangsha University of Science and TechnologyChangshaChina

Personalised recommendations